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. 2022 Apr 22;19(9):5099.
doi: 10.3390/ijerph19095099.

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

Farrukh Saleem  1 , Abdullah Saad Al-Malaise Al-Ghamdi  1 , Madini O Alassafi  2 , Saad Abdulla AlGhamdi  3
Affiliations

Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review

Farrukh Saleem et al. Int J Environ Res Public Health. .

Abstract

COVID-19 is a disease caused by SARS-CoV-2 and has been declared a worldwide pandemic by the World Health Organization due to its rapid spread. Since the first case was identified in Wuhan, China, the battle against this deadly disease started and has disrupted almost every field of life. Medical staff and laboratories are leading from the front, but researchers from various fields and governmental agencies have also proposed healthy ideas to protect each other. In this article, a Systematic Literature Review (SLR) is presented to highlight the latest developments in analyzing the COVID-19 data using machine learning and deep learning algorithms. The number of studies related to Machine Learning (ML), Deep Learning (DL), and mathematical models discussed in this research has shown a significant impact on forecasting and the spread of COVID-19. The results and discussion presented in this study are based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Out of 218 articles selected at the first stage, 57 met the criteria and were included in the review process. The findings are therefore associated with those 57 studies, which recorded that CNN (DL) and SVM (ML) are the most used algorithms for forecasting, classification, and automatic detection. The importance of the compartmental models discussed is that the models are useful for measuring the epidemiological features of COVID-19. Current findings suggest that it will take around 1.7 to 140 days for the epidemic to double in size based on the selected studies. The 12 estimates for the basic reproduction range from 0 to 7.1. The main purpose of this research is to illustrate the use of ML, DL, and mathematical models that can be helpful for the researchers to generate valuable solutions for higher authorities and the healthcare industry to reduce the impact of this epidemic.

Keywords: basic reproduction rate; deep learning; epidemiology of COVID-19; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Study Selection Workflow based on PRISMA.
Figure 2
Figure 2
Selected Studies Publishing Journals.
Figure 3
Figure 3
Region of Selected Studies.
Figure 4
Figure 4
Research Domain Classification.
Figure 5
Figure 5
Types of Modeling in Selected Studies.
Figure 6
Figure 6
Ratio of ML Models in Selected Studies.
Figure 7
Figure 7
Ratio of DL Models in Selected Studies.
Figure 8
Figure 8
Ratio of Mathematical and Regression Models in Selected Studies.
Figure 9
Figure 9
Ratio of Validation Strategies in Selected Studies.
Figure 10
Figure 10
List of Evaluation Metrics used in the Selected Studies.
See this image and copyright information in PMC

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